OpenRaiser/Intern-Atlas huggingface dataset:
Generative AI and the Future of the Digital Commons: Five Open Questions and Knowledge Gaps
Venue: arXiv cs.CY 2025
Year: 2025

The rapid advancement of Generative AI (GenAI) relies heavily on the digital commons, a vast collection of free and open online content that is created, shared, and maintained by communities. However, this relationship is becoming increasingly strained due to financial burdens, decreased contributions, and misalignment between AI models and community norms. As we move deeper into the GenAI era, it is essential to examine the interdependent relationship between GenAI, the long-term sustainability of the digital commons, and the equity of current AI development practices. We highlight five critical questions that require urgent attention: 1. How can we prevent the digital commons from being threatened by undersupply as individuals cease contributing to the commons and turn to Generative AI for information? 2. How can we mitigate the risk of the open web closing due to restrictions on access to curb AI crawlers? 3. How can technical standards and legal frameworks be updated to reflect the evolving needs of organizations hosting common content? 4. What are the effects of increased synthetic content in open knowledge databases, and how can we ensure their integrity? 5. How can we account for and distribute the infrastructural and environmental costs of providing data for AI training? We emphasize the need for more responsible practices in AI development, recognizing the digital commons not only as content but as a collaborative and decentralized form of knowledge governance, which relies on the practice of "commoning" - making, maintaining, and protecting shared and open resources. Ultimately, our goal is to stimulate discussion and research on the intersection of Generative AI and the digital commons, with the aim of developing an "AI commons" and public infrastructures for AI development that support the long-term health of the digital commons.
Post-training for Efficient Communication via Convention Formation
Venue: arXiv cs.CL 2025
Year: 2025

Humans communicate with increasing efficiency in multi-turn interactions, by adapting their language and forming ad-hoc conventions. In contrast, prior work shows that LLMs do not naturally show this behavior. We develop a post-training process to develop this ability through targeted fine-tuning on heuristically identified demonstrations of convention formation. We evaluate with two new benchmarks focused on this capability. First, we design a focused, cognitively-motivated interaction benchmark that consistently elicits strong convention formation trends in humans. Second, we create a new document-grounded reference completion task that reflects in-the-wild convention formation behavior. Our studies show significantly improved convention formation abilities in post-trained LLMs across the two evaluation methods.
Med-GRIM: Enhanced Zero-Shot Medical VQA using prompt-embedded Multimodal Graph RAG
Venue: arXiv cs.CV 2025
Year: 2025

An ensemble of trained multimodal encoders and vision-language models (VLMs) has become a standard approach for visual question answering (VQA) tasks. However, such models often fail to produce responses with the detailed precision necessary for complex, domain-specific applications such as medical VQA. Our representation model, BIND: BLIVA Integrated with Dense Encoding, extends prior multimodal work by refining the joint embedding space through dense, query-token-based encodings inspired by contrastive pretraining techniques. This refined encoder powers Med-GRIM, a model designed for medical VQA tasks that leverages graph-based retrieval and prompt engineering to integrate domain-specific knowledge. Rather than relying on compute-heavy fine-tuning of vision and language models on specific datasets, Med-GRIM applies a low-compute, modular workflow with small language models (SLMs) for efficiency. Med-GRIM employs prompt-based retrieval to dynamically inject relevant knowledge, ensuring both accuracy and robustness in its responses. By assigning distinct roles to each agent within the VQA system, Med-GRIM achieves large language model performance at a fraction of the computational cost. Additionally, to support scalable research in zero-shot multimodal medical applications, we introduce DermaGraph, a novel Graph-RAG dataset comprising diverse dermatological conditions. This dataset facilitates both multimodal and unimodal querying. The code and dataset are available at: https://github.com/Rakesh-123-cryp/Med-GRIM.git
Optimization of Flip-Landing Trajectories for Starship based on a Deep Learned Simulator
Venue: arXiv cs.RO 2025
Year: 2025

We propose a differentiable optimization framework for flip-and-landing trajectory design of reusable spacecraft, exemplified by the Starship vehicle. A deep neural network surrogate, trained on high-fidelity CFD data, predicts aerodynamic forces and moments, and is tightly coupled with a differentiable rigid-body dynamics solver. This enables end-to-end gradient-based trajectory optimization without linearization or convex relaxation. The framework handles actuator limits and terminal landing constraints, producing physically consistent, optimized control sequences. Both standard automatic differentiation and Neural ODEs are applied to support long-horizon rollouts. Results demonstrate the framework's effectiveness in modeling and optimizing complex maneuvers with high nonlinearities. This work lays the groundwork for future extensions involving unsteady aerodynamics, plume interactions, and intelligent guidance design.
Large Language Models Facilitate Vision Reflection in Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents several novel findings on the explainability of vision reflection in large multimodal models (LMMs). First, we show that prompting an LMM to verify the prediction of a specialized vision model can improve recognition accuracy, even on benchmarks like ImageNet, despite prior evidence that LMMs typically underperform dedicated vision encoders. Second, we analyze the internal behavior of vision reflection and find that the vision-language connector maps visual features into explicit textual concepts, allowing the language model to reason about prediction plausibility using commonsense knowledge. We further observe that replacing a large number of vision tokens with only a few text tokens still enables LLaVA to generate similar answers, suggesting that LMMs may rely primarily on a compact set of distilled textual representations rather than raw vision features. Third, we show that a training-free connector can enhance LMM performance in fine-grained recognition tasks, without extensive feature-alignment training. Together, these findings offer new insights into the explainability of vision-language models and suggest that vision reflection is a promising strategy for achieving robust and interpretable visual recognition.
Self-Organizing Survival Manifolds: A Theory for Unsupervised Discovery of Prognostic Structures in Biological Systems
Venue: arXiv cs.LG 2025
Year: 2025

Survival is traditionally modeled as a supervised learning task, reliant on curated outcome labels and fixed covariates. This work rejects that premise. It proposes that survival is not an externally annotated target but a geometric consequence: an emergent property of the curvature and flow inherent in biological state space. We develop a theory of Self-Organizing Survival Manifolds (SOSM), in which survival-relevant dynamics arise from low-curvature geodesic flows on latent manifolds shaped by internal biological constraints. A survival energy functional based on geodesic curvature minimization is introduced and shown to induce structures where prognosis aligns with geometric flow stability. We derive discrete and continuous formulations of the objective and prove theoretical results demonstrating the emergence and convergence of survival-aligned trajectories under biologically plausible conditions. The framework draws connections to thermodynamic efficiency, entropy flow, Ricci curvature, and optimal transport, grounding survival modeling in physical law. Health, disease, aging, and death are reframed as geometric phase transitions in the manifold's structure. This theory offers a universal, label-free foundation for modeling survival as a property of form, not annotation-bridging machine learning, biophysics, and the geometry of life itself.
A tutorial note on collecting simulated data for vision-language-action models
Venue: arXiv cs.RO 2025
Year: 2025

Traditional robotic systems typically decompose intelligence into independent modules for computer vision, natural language processing, and motion control. Vision-Language-Action (VLA) models fundamentally transform this approach by employing a single neural network that can simultaneously process visual observations, understand human instructions, and directly output robot actions -- all within a unified framework. However, these systems are highly dependent on high-quality training datasets that can capture the complex relationships between visual observations, language instructions, and robotic actions. This tutorial reviews three representative systems: the PyBullet simulation framework for flexible customized data generation, the LIBERO benchmark suite for standardized task definition and evaluation, and the RT-X dataset collection for large-scale multi-robot data acquisition. We demonstrated dataset generation approaches in PyBullet simulation and customized data collection within LIBERO, and provide an overview of the characteristics and roles of the RT-X dataset for large-scale multi-robot data acquisition.
Communication-Learning Co-Design for Differentially Private Over-the-Air Federated Distillation
Venue: arXiv cs.IT 2025
Year: 2025

The ever-growing learning model size nowadays challenges the communication efficiency and privacy preservation of the traditional federated learning (FL). In this paper, we propose a novel differentially private (DP) over-the-air federated distillation (FD) framework, where wireless devices (WDs) periodically share noise-perturbed model outputs with the parameter server by harnessing the superposition property of multi-access channels. Accordingly, over-the-air FD enables the shared responsibility of the DP preservation on the low-dimensional disclosed signals among WDs. We study the communication-learning co-design problem in differentially private over-the-air FD, aiming to maximize the learning convergence rate while meeting the transmit power and DP requirements of WDs. The main challenge is rooted in the intractable learning and privacy analysis in over-the-air FD, together with the strong coupling among the decision variables spanning two timescales. To tackle this problem, we first derive the analytical learning convergence rate and privacy losses of WDs, based on which the optimal transceiver design per FD round and long-term training rounds decision are obtained in the closed forms. Numerical results demonstrate that the proposed differentially private over-the-air FD approach achieves a better learning-privacy trade-off with largely-reduced communication overhead than the conventional FL benchmarks.
Robust and Agile Quadrotor Flight via Adaptive Unwinding-Free Quaternion Sliding Mode Control
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a new adaptive sliding mode control (SMC) framework for quadrotors that achieves robust and agile flight under tight computational constraints. The proposed controller addresses key limitations of prior SMC formulations, including (i) the slow convergence and almost-global stability of $\mathrm{SO(3)}$-based methods, (ii) the oversimplification of rotational dynamics in Euler-based controllers, (iii) the unwinding phenomenon in quaternion-based formulations, and (iv) the gain overgrowth problem in adaptive SMC schemes. Leveraging nonsmooth stability analysis, we provide rigorous global stability proofs for both the nonsmooth attitude sliding dynamics defined on $\mathbb{S}^3$ and the position sliding dynamics. Our controller is computationally efficient and runs reliably on a resource-constrained nano quadrotor, achieving 250 Hz and 500 Hz refresh rates for position and attitude control, respectively. In an extensive set of hardware experiments with over 130 flight trials, the proposed controller consistently outperforms three benchmark methods, demonstrating superior trajectory tracking accuracy and robustness with relatively low control effort. The controller enables aggressive maneuvers such as dynamic throw launches, flip maneuvers, and accelerations exceeding 3g, which is remarkable for a 32-gram nano quadrotor. These results highlight promising potential for real-world applications, particularly in scenarios requiring robust, high-performance flight control under significant external disturbances and tight computational constraints.
Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loop
Venue: arXiv cs.AI 2025
Year: 2025

The history of science is punctuated by serendipitous discoveries, where unexpected observations, rather than targeted hypotheses, opened new fields of inquiry. While modern autonomous laboratories excel at accelerating hypothesis testing, their optimization for efficiency risks overlooking these crucial, unplanned findings. To address this gap, we introduce SciLink, an open-source, multi-agent artificial intelligence framework designed to operationalize serendipity in materials research by creating a direct, automated link between experimental observation, novelty assessment, and theoretical simulations. The framework employs a hybrid AI strategy where specialized machine learning models perform quantitative analysis of experimental data, while large language models handle higher-level reasoning. These agents autonomously convert raw data from materials characterization techniques into falsifiable scientific claims, which are then quantitatively scored for novelty against the published literature. We demonstrate the framework's versatility across diverse research scenarios, showcasing its application to atomic-resolution and hyperspectral data, its capacity to integrate real-time human expert guidance, and its ability to close the research loop by proposing targeted follow-up experiments. By systematically analyzing all observations and contextualizing them, SciLink provides a practical framework for AI-driven materials research that not only enhances efficiency but also actively cultivates an environment ripe for serendipitous discoveries, thereby bridging the gap between automated experimentation and open-ended scientific exploration.
ContextGuard-LVLM: Enhancing News Veracity through Fine-grained Cross-modal Contextual Consistency Verification
Venue: arXiv cs.CV 2025
Year: 2025

The proliferation of digital news media necessitates robust methods for verifying content veracity, particularly regarding the consistency between visual and textual information. Traditional approaches often fall short in addressing the fine-grained cross-modal contextual consistency (FCCC) problem, which encompasses deeper alignment of visual narrative, emotional tone, and background information with text, beyond mere entity matching. To address this, we propose ContextGuard-LVLM, a novel framework built upon advanced Vision-Language Large Models (LVLMs) and integrating a multi-stage contextual reasoning mechanism. Our model is uniquely enhanced through reinforced or adversarial learning paradigms, enabling it to detect subtle contextual misalignments that evade zero-shot baselines. We extend and augment three established datasets (TamperedNews-Ent, News400-Ent, MMG-Ent) with new fine-grained contextual annotations, including "contextual sentiment," "visual narrative theme," and "scene-event logical coherence," and introduce a comprehensive CTXT (Contextual Coherence) entity type. Extensive experiments demonstrate that ContextGuard-LVLM consistently outperforms state-of-the-art zero-shot LVLM baselines (InstructBLIP and LLaVA 1.5) across nearly all fine-grained consistency tasks, showing significant improvements in complex logical reasoning and nuanced contextual understanding. Furthermore, our model exhibits superior robustness to subtle perturbations and a higher agreement rate with human expert judgments on challenging samples, affirming its efficacy in discerning sophisticated forms of context detachment.
Do Biased Models Have Biased Thoughts?
Venue: arXiv cs.CL 2025
Year: 2025

The impressive performance of language models is undeniable. However, the presence of biases based on gender, race, socio-economic status, physical appearance, and sexual orientation makes the deployment of language models challenging. This paper studies the effect of chain-of-thought prompting, a recent approach that studies the steps followed by the model before it responds, on fairness. More specifically, we ask the following question: $\textit{Do biased models have biased thoughts}$? To answer our question, we conduct experiments on $5$ popular large language models using fairness metrics to quantify $11$ different biases in the model's thoughts and output. Our results show that the bias in the thinking steps is not highly correlated with the output bias (less than $0.6$ correlation with a $p$-value smaller than $0.001$ in most cases). In other words, unlike human beings, the tested models with biased decisions do not always possess biased thoughts.
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
Venue: arXiv cs.LG 2025
Year: 2025

Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.
Refactoring-Aware Patch Integration Across Structurally Divergent Java Forks
Venue: arXiv cs.SE 2025
Year: 2025

While most forks on platforms like GitHub are short-lived and used for social collaboration, a smaller but impactful subset evolve into long-lived forks, referred to here as variants, that maintain independent development trajectories. Integrating bug-fix patches across such divergent variants poses challenges due to structural drift, including refactorings that rename, relocate, or reorganize code elements and obscure semantic correspondence. This paper presents an empirical study of patch integration failures in 14 divergent pair of variants and introduces RePatch, a refactoring-aware integration system for Java repositories. RePatch extends the RefMerge framework, originally designed for symmetric merges, by supporting asymmetric patch transfer. RePatch inverts refactorings in both the source and target to realign the patch context, applies the patch, and replays the transformations to preserve the intent of the variant. In our evaluation of 478 bug-fix pull requests, Git cherry-pick fails in 64.4% of cases due to structural misalignments, while RePatch successfully integrates 52.8% of the previously failing patches. These results highlight the limitations of syntax-based tools and the need for semantic reasoning in variant-aware patch propagation.
Improved Obstacle Avoidance for Autonomous Robots with ORCA-FLC
Venue: arXiv cs.RO 2025
Year: 2025

Obstacle avoidance enables autonomous agents and robots to operate safely and efficiently in dynamic and complex environments, reducing the risk of collisions and damage. For a robot or autonomous system to successfully navigate through obstacles, it must be able to detect such obstacles. While numerous collision avoidance algorithms like the dynamic window approach (DWA), timed elastic bands (TEB), and reciprocal velocity obstacles (RVO) have been proposed, they may lead to suboptimal paths due to fixed weights, be computationally expensive, or have limited adaptability to dynamic obstacles in multi-agent environments. Optimal reciprocal collision avoidance (ORCA), which improves on RVO, provides smoother trajectories and stronger collision avoidance guarantees. We propose ORCA-FL to improve on ORCA by using fuzzy logic controllers (FLCs) to better handle uncertainty and imprecision for obstacle avoidance in path planning. Numerous multi-agent experiments are conducted and it is shown that ORCA-FL can outperform ORCA in reducing the number of collision if the agent has a velocity that exceeds a certain threshold. In addition, a proposed algorithm for improving ORCA-FL using fuzzy Q reinforcement learning (FQL) is detailed for optimizing and tuning FLCs.
Robust-Sub-Gaussian Model Predictive Control for Safe Ultrasound-Image-Guided Robotic Spinal Surgery
Venue: arXiv cs.RO 2025
Year: 2025

Safety-critical control using high-dimensional sensory feedback from optical data (e.g., images, point clouds) poses significant challenges in domains like autonomous driving and robotic surgery. Control can rely on low-dimensional states estimated from high-dimensional data. However, the estimation errors often follow complex, unknown distributions that standard probabilistic models fail to capture, making formal safety guarantees challenging. In this work, we introduce a novel characterization of these general estimation errors using sub-Gaussian noise with bounded mean. We develop a new technique for uncertainty propagation of proposed noise characterization in linear systems, which combines robust set-based methods with the propagation of sub-Gaussian variance proxies. We further develop a Model Predictive Control (MPC) framework that provides closed-loop safety guarantees for linear systems under the proposed noise assumption. We apply this MPC approach in an ultrasound-image-guided robotic spinal surgery pipeline, which contains deep-learning-based semantic segmentation, image-based registration, high-level optimization-based planning, and low-level robotic control. To validate the pipeline, we developed a realistic simulation environment integrating real human anatomy, robot dynamics, efficient ultrasound simulation, as well as in-vivo data of breathing motion and drilling force. Evaluation results in simulation demonstrate the potential of our approach for solving complex image-guided robotic surgery task while ensuring safety.